An AI workflow is the end-to-end sequence used to turn a problem and its data into a functioning artificial intelligence system. Depending on the application, it may include data collection, cleaning, labeling, model selection, training, evaluation, deployment, monitoring, and feedback. Generative AI workflows can also add retrieval, prompt construction, tool calls, guardrails, and human approval steps. Mapping the workflow makes dependencies and failure points visible: poor inputs can undermine a strong model, while missing monitoring can allow quality to decline unnoticed. Well-designed workflows define ownership, measurable acceptance criteria, data-handling rules, and fallback behavior for cases the system cannot handle safely.
OpenAI and Databricks have forged a multiyear $100 million partnership to embed powerful AI agents into enterprise data platforms, enabling organizations to build agents using their own data.
Google is integrating Gemini AI into Chrome for U.S. users, bringing features like multi-tab summarization, agentic task automation, and deeper sync with Google apps.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.
IBM has unveiled a detailed development plan for Quantum Starling, the world’s first large-scale fault-tolerant quantum computer, aiming for deployment by 2029 – setting a milestone for practical quantum capability.